Building on Multi-Model Databases

Chapter 8. Summary

The rapid growth of data, including the digitization of human communication, has created a proliferation of data silos throughout enterprises. Trying to see across these silos—creating a 360-degree view—has been an arduous task, if not a losing battle, as companies spend untold millions trying to buy tools that help them parse data in the traditional, relational way.

The challenge is integrating data from silos:

• ETL and schema-first systems are the enemy of progress and getting things done.

• We are going to use many models (relational, mainframe, text, graph, document, key-value).

• We are going to use multiple formats (JSON, XML, text, binary).

• Much of our data actually comes structured from relational tables, but the same entity type can be modeled in different ways across multiple different silos.

• The natural approach has been for our people to code their way out of the problem of many models and polyglot persistence with many technical silos.

• The next step is to move the complexity into multi-model database management systems (DBMS) products that load as is.

• This means new products, new evaluation criteria, and new (higher) expectations for DBMS as we move forward and evolve.

• A significant unlearning of biases and assumptions is required.

• We will be introducing new products into existing architectures.

• Change management will be affected because when we do the work and how quickly we accomplish it will change.

In addition to the data, the context of data is ...